


"""simple docstring"""


import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class       A__						(    lowercase__			,  unittest.TestCase):


     """simple docstring"""
     snake_case__						:       List[Any]				    =DiTPipeline
     snake_case__						:       Optional[Any]				    =CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
     snake_case__						:       int				    =PipelineTesterMixin.required_optional_params - {
         '''latents''',
         '''num_images_per_prompt''',
         '''callback''',
         '''callback_steps''',
     }
     snake_case__						:       int				    =CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
     snake_case__						:       List[Any]				    =False

     def 		a__    (						self:    List[str]						)->     str:
           torch.manual_seed(0						)
           lowerCamelCase       :       str   =						TransformeraDModel(
               sample_size=16  ,		num_layers=2  ,		patch_size=4  ,		attention_head_dim=8  ,		num_attention_heads=2  ,		in_channels=4  ,		out_channels=8  ,		attention_bias=SCREAMING_SNAKE_CASE_  ,		activation_fn="""gelu-approximate"""  ,		num_embeds_ada_norm=1_000  ,		norm_type="""ada_norm_zero"""  ,		norm_elementwise_affine=SCREAMING_SNAKE_CASE_  ,		)
           lowerCamelCase       :       List[str]   =						AutoencoderKL()
           lowerCamelCase       :       List[Any]   =						DDIMScheduler()
           lowerCamelCase       :       Optional[int]   =						{"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
           return components

     def 		a__    (						self:    str  ,		__a:    int  ,		__a:    str=0						)->     Tuple:


           if str(SCREAMING_SNAKE_CASE_						).startswith("""mps"""						):
                 lowerCamelCase       :       str   =						torch.manual_seed(SCREAMING_SNAKE_CASE_						)
           else:
                 lowerCamelCase       :       Dict   =						torch.Generator(device=SCREAMING_SNAKE_CASE_						).manual_seed(SCREAMING_SNAKE_CASE_						)
           lowerCamelCase       :       Tuple   =						{
               """class_labels""": [1],
               """generator""": generator,
               """num_inference_steps""": 2,
               """output_type""": """numpy""",
           }
           return inputs

     def 		a__    (						self:    List[str]						)->     Union[str, Any]:
           lowerCamelCase       :       str   =						"""cpu"""

           lowerCamelCase       :       Dict   =						self.get_dummy_components()
           lowerCamelCase       :       Union[str, Any]   =						self.pipeline_class(**SCREAMING_SNAKE_CASE_						)
           pipe.to(SCREAMING_SNAKE_CASE_						)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_						)

           lowerCamelCase       :       Optional[Any]   =						self.get_dummy_inputs(SCREAMING_SNAKE_CASE_						)
           lowerCamelCase       :       Union[str, Any]   =						pipe(**SCREAMING_SNAKE_CASE_						).images
           lowerCamelCase       :       List[str]   =						image[0, -3:, -3:, -1]

           self.assertEqual(image.shape  ,		(1, 16, 16, 3)						)
           lowerCamelCase       :       Any   =						np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57]						)
           lowerCamelCase       :       List[str]   =						np.abs(image_slice.flatten() - expected_slice						).max()
           self.assertLessEqual(SCREAMING_SNAKE_CASE_  ,		1e-3						)

     def 		a__    (						self:    Optional[int]						)->     int:
           self._test_inference_batch_single_identical(relax_max_difference=SCREAMING_SNAKE_CASE_  ,		expected_max_diff=1e-3						)







     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()  ,		reason="""XFormers attention is only available with CUDA and `xformers` installed"""  ,		)
     def 		a__    (						self:    Union[str, Any]						)->     Union[str, Any]:
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)


@require_torch_gpu
@slow
class       A__						(    unittest.TestCase):


     """simple docstring"""

     def 		a__    (						self:    Union[str, Any]						)->     Dict:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()

     def 		a__    (						self:    Dict						)->     Optional[int]:
           lowerCamelCase       :       Dict   =						torch.manual_seed(0						)

           lowerCamelCase       :       Tuple   =						DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""						)
           pipe.to("""cuda"""						)

           lowerCamelCase       :       Optional[int]   =						["""vase""", """umbrella""", """white shark""", """white wolf"""]
           lowerCamelCase       :       List[Any]   =						pipe.get_label_ids(SCREAMING_SNAKE_CASE_						)

           lowerCamelCase       :       Dict   =						pipe(SCREAMING_SNAKE_CASE_  ,		generator=SCREAMING_SNAKE_CASE_  ,		num_inference_steps=40  ,		output_type="""np"""						).images

           for word, image in zip(SCREAMING_SNAKE_CASE_  ,		SCREAMING_SNAKE_CASE_						):
                 lowerCamelCase       :       Optional[Any]   =						load_numpy(
                     f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'						)
                 assert np.abs((expected_image - image).max()						) < 1e-2







     def 		a__    (						self:    Any						)->     int:
           lowerCamelCase       :       Optional[int]   =						DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""						)
           lowerCamelCase       :       List[Any]   =						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
           pipe.to("""cuda"""						)

           lowerCamelCase       :       str   =						["""vase""", """umbrella"""]
           lowerCamelCase       :       Tuple   =						pipe.get_label_ids(SCREAMING_SNAKE_CASE_						)

           lowerCamelCase       :       Union[str, Any]   =						torch.manual_seed(0						)
           lowerCamelCase       :       Optional[Any]   =						pipe(SCREAMING_SNAKE_CASE_  ,		generator=SCREAMING_SNAKE_CASE_  ,		num_inference_steps=25  ,		output_type="""np"""						).images

           for word, image in zip(SCREAMING_SNAKE_CASE_  ,		SCREAMING_SNAKE_CASE_						):
                 lowerCamelCase       :       Optional[int]   =						load_numpy(
                     """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                     f'/dit/{word}_512.npy'						)

                 assert np.abs((expected_image - image).max()						) < 1e-1






"""simple docstring"""






UpperCAmelCase        =				8.3_144_598




def __magic_name__	(				_lowerCamelCase:		float,					_lowerCamelCase:		float			)					->						float:

	'''simple docstring'''




	if temperature < 0:
		raise Exception('''Temperature cannot be less than 0 K'''			)
	if molar_mass <= 0:
		raise Exception('''Molar mass cannot be less than or equal to 0 kg/mol'''			)
	else:
		return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
					import doctest

					# run doctest
					doctest.testmod()

					# example
					UpperCAmelCase        =				3_0_0
					UpperCAmelCase        =				2_8
					UpperCAmelCase        =				rms_speed_of_molecule(temperature, molar_mass)
					print(f"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")


